Multi-Location Search Visibility: Winning In Google & AI

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Multi-location brands are currently reviewing their Google Search Console click traffic, comparing 2026 to 2025, and trying to convince themselves and key stakeholders that AI Overviews are responsible for a year-over-year drop in non-branded clicks.

Today, visibility is distributed across a multitude of destinations, including features in Google Maps such as “Ask Maps,” AI Overviews, AI Mode, ChatGPT, Gemini, Perplexity, Apple Maps, and social search.

The challenge for multi-location brands is that while more locations create more opportunities, they also create more complexity. This is why enterprise and franchise brands require a completely different approach than single-location businesses.

Building on fundamentals, we’re going to explore how we leverage AI to improve our data, landing pages, citations, and reputation. We’re going to discover how to replicate our website content strategy across the web, natively within each discoverability opportunity beyond Google alone.

The Modern Local Discover Ecosystem #

With agentic technology emerging, there may even be a point in time where users rarely visit our website at all, as the platforms will provide the appropriate integrations for users to transact directly within them.

The new Local Search Supply Chain includes traditional elements, such as our brand website, business listings, data aggregators, and industry directories, as well as review platforms and user-generated content.

The role of knowledge graphs and entity understanding is increasingly important. Which means, if you’re leaning on an industry data management platform that’s staying ahead, such as Yext, Rio SEO, Birdeye, SOCi, or Locl, you’re already one step ahead.

From what we can tell, AI systems need the following to recommend a business:

Trusted business information: N.A.P. beyond the old roster of directories.Location-specific relevance: Supported by user-generated content.Strong reputation signals: Beyond Google Maps and Yelp.Third-party validation: Neglected industry directories we should have paid closer attention to.And clear entity relationships: Think “Semantic Triples” (QDOBA → offers → burritos, for example).

From Rankings To Recommendations #

As we wrap our heads around this “evolution of search visibility,” a common perception is that traditional SEO focused on rankings, where modern discovery focuses on recommendations.

At a very broad level, the experience differences can be broken down into the following stages:

The new visibility question isn’t “How do we rank No. 1?” It’s “How do we become the business AI recommends?” From what we can tell so far, these recommendation engines appear to favor accurate business data, review quality and volume, strong location pages, consistent citations, and clear entity signals.

4 Pillars Of Multi-Location Search Visibility #

Pillar 1: Business Data Accuracy & Consistency

In terms of trust, local business data remains the foundation of local visibility. This includes elements such as:

Name, address, phone number.
Hours of operation.
Categories. Attributes, amenities.- Products, services.

This is where those platforms mentioned above come into play. Common challenges for multi-location brands include rebrands, franchise ownership change (if franchised), duplicate listings, and inconsistent updates.

AI platforms can be used to search for and identify inconsistencies. Not just in business directories, but in destinations uncovered by peeking at sources cited in large language model recommendations. Many emerging “AI ranking platforms” offer this feature to help teams determine where to allocate time and resources. Surprisingly, Yelp and Reddit show up less than you might imagine.

Pillar 1 Action Items:

Leverage AI to uncover data inconsistencies.
Ensure every field is optimized and consistent across the web.
Work with your data management platform to address at scale where possible.

Pillar 2: Location Page Quality & Relevance

If you got a chill when we mentioned “UGC” earlier, this pillar should do the opposite, as your location pages are owned and managed by you. Optimizing your location landing page (LLP) and intent or specialty pages seems simple enough until you start pushing them through brand and legal teams, dev teams, and asset management governance.

A quick search in ChatGPT reveals just how important your own content can be for trust and visibility signals. IHOP, for example, scales content across over 1,400 locations by simply creating landing pages showcasing elements that align with business objectives, such as off-premises (takeout, delivery, catering), while also addressing specials, restaurant jobs, and even menu items.

A bonus benefit of these intent pages is the increase in entity relationships between the brand and its products and services. The search site links are also not too hard on the eyes – especially when coupled with paid search using the same site link asset strategy.

Following IHOP’s example, URLs may include something like the following:

/ca/norwalk/breakfast-12623-norwalk-blvd-939
/ca/norwalk/breakfast-12623-norwalk-blvd-939/burgers
/ca/norwalk/breakfast-12623-norwalk-blvd-939/careers
/ca/norwalk/breakfast-12623-norwalk-blvd-939/delivery
/ca/norwalk/breakfast-12623-norwalk-blvd-939/late-night-food
/ca/norwalk/breakfast-12623-norwalk-blvd-939/omelettes
/ca/norwalk/breakfast-12623-norwalk-blvd-939/pancakes
/ca/norwalk/breakfast-12623-norwalk-blvd-939/specials
/ca/norwalk/breakfast-12623-norwalk-blvd-939/takeout
/ca/norwalk/breakfast-12623-norwalk-blvd-939/waffles

Beyond the intent pages, there are multiple elements of your primary LLP that play into traditional search signals as well as modern search visibility. These attributes are ranked below based on a location page study and updated with their benefit towards LLM discoverability.

Possible Ranking Signal | Description | By % | Hyperlocal Content | Unique content about the specific city, neighborhood, landmarks, events, and service area helps demonstrate local relevance beyond a templated location page. From an LLM discoverability standpoint, leveraging semantic triples and addressing long-tail queries can increase the probability of being recommended. Example from IHOP’s Norwalk, CA page: | 107% | Custom Location Images | Original photos of the location, staff, storefront, service area, or nearby landmarks help reinforce geographic relevance and authenticity. | 84% | Location Social Links | Linking to location-specific social profiles helps establish entity consistency and local business legitimacy across platforms. The most common for multi-location businesses are Facebook, Yelp, TripAdvisor, and Nextdoor. When coupled with the SameAs schema (structured markup), these links can help | 50% | Directions Link | Providing driving directions or a map link (preferably to Google Maps) improves local usability signals and reinforces the physical location connection. | 16% | Page Size | Pages with more comprehensive local information, services, FAQs, and supporting content may improve relevance. There is no direct benefit of page size, but a page with more comprehensive information might benefit from visibility in traditional search results. | 14% | Fully Loaded Time | Faster- pages provide a better user experience and may contribute indirectly to stronger engagement signals. This is often handled through delaying scripts, using modern image formats, and a content delivery network (CDN) such as Cloudflare. | 10% | Open Now Status | Displaying current operating status helps users quickly determine availability and may improve local page usefulness. Imagine a prompt within an LLM that includes “that’s open right now.” Your website is a single source of truth for search engines and LLMs alike. | 10% | PageSpeed | Similar to Fully Loaded Time, |

Native Reviews For LLMs, customer feedback and UGC provide an extra layer of trust.

Hours Listed Similar to the Open Now attribute, well-organized, easy-to-retrieve hours of operation can help address prompts that include time of day.

We’ve just scratched the surface when it comes to elements to test. For example, I’d love to experiment with adding a carousel of Instagram Reels from customers. Or even test embedding AI features that allow users to customize food orders based on prompts, geography, and personalization.

Pillar 2 Action Items:

Leverage AI to uncover landing page opportunities based on the competitive landscape.
Discuss and research appropriate intent/specialty pages based on business objectives.
Schedule tests and rollouts of the attributes above with content and dev teams.

Pillar 3: Ecosystem Visibility & Third-Party Validation

In modern search, one marketing objective is to think beyond the Google Business Profile. In the same way that many businesses switched their door placards and point-of-sale reminders from Yelp to Google reviews, today we’re testing moving towards earning user-generated content where LLMs cite their top recommendations from.

In traditional multi-location search, we referred to this as “citation-building.” Today, we’re calling it “brand mentions” or “brand citations,” where NAP visibility blends with sentiment, and the opportunity to be recommended based on fresh mentions and citations that contain referenceable statements.

For example, a new burger place in Buena Park got our attention after scrolling through dozens of burger joints in the Buena Park, California area. The place was empty, but the staff was super friendly, and the burgers (smashburgers to be exact) were extraordinary. As a test, we left a review specifically using expanded upon semantic triples (subject → predicate → object), such as “Good Buns is a hidden gem serving the best burgers in Buena Park.”

That statement alone was enough to propel the listing to the top of the Ask Maps recommendations. Imagine if users continued to say the same thing in the same format across multiple platforms, including Reddit, social media, and perhaps even YouTube. Fresh references, real humans, increasing ratios over time. It’s that simple.

In Pillar 1, the importance of consistent data and utilizing all available fields was mentioned. This applies in Pillar 3 as well, especially entity mentions adjacent to entities we want our locations to be well known for. Only now, it’s our customers who do most of the work, inspired by our reminders to share their experiences, while dropping subtle hints as to what to say.

When added to traditional multi-location SEO data visibility, our list looks more like this, where bolded destinations should be handled by your data management platform:

Aggregators: Data Axle, Localeze, Foursquare.Search Engines: Google Maps, Bing Places.Navigation Engines: HERE Technologies, Apple Maps, MapQuest.Local Social: Facebook, TripAdvisor, Yelp.Industry Directories: Avvo, Thumbtack, Healthgrades.Local Directories: Chambers of Commerce, city guides, tourist information sites.LLM Citation Sources: When overlapping between locations in different areas.

When researching, tools such as the Whitespark and GeoRanker Local Citation Finder tools can be helpful, provided you’re running 10+ locations in different cities to identify overlaps, where the directory or destination covers more than a specific city or region.

Pillar 3 Action Items:

For traditional search (used by LLM “RAG” processes), ensure all destinations listed above are addressed with your data management platform.
Research LLM citation sources for local queries, and add applicable opportunities to the roadmap. Repeat for traditional search using Whitespark or GeoRanker.
Test point-of-sale brand mentions, such as language on uniforms, wall art, or table art, with a goal of having customers mention us based on the citation research.

Pillar 4: Reputation & Trust Signals

As mentioned earlier, it’s time to break out of the “Leave a Review on Google” bubble and expand our horizons to influence our visibility in LLMs. OpenAI (ChatGPT) has never made a public statement about ingesting Google Maps reviews when making recommendations. In fact, in Bing Places and Yahoo! Local, you’ll find Yelp reviews, as well as within MapQuest, and yes, Apple Maps. With the RAG process in ChatGPT using Bing, your Google reviews may have lost some value.

Yet, for some reason, enterprise brands are still hooked on Google reviews.

In Pillar 3, we mentioned Ask Maps, a new AI feature within Google Maps that extends beyond the platform’s own review system by searching the web for sentiment, clear product and service mentions, and other business review platforms. Even then, it was one review mentioning “best burgers in Buena Park” that led the AI to recommend Good Buns.

There isn’t a reason today not to expand our reputation management objectives beyond Google Maps alone.

For businesses in niche markets, such as lawyers, home services, healthcare, and others, you’ll find several opportunities to send customers where it matters to ChatGPT and other LLMs.

AI can assist where platforms leave off with tracking ratings and reviews on industry business review platforms. These vary by niche and can be uncovered in Pillar 3 through research into citation sources, brand mentions, and competitive research.

Examples of niche business review destinations:

Some data management platforms have included sentiment analysis capabilities even before the growth of AI. Today’s platforms offer advanced AI-driven sentiment analysis that provides valuable insights to guide product improvement, staff training opportunities, and business development, not only improving the business but also supporting review quality improvements.

Sentiment analysis can also be performed manually, provided you have identified and backed up your reviews. Below is an example of using Perplexity Computer to get two stakeholder-ready reports.

Try this prompt in Perplexity Computer (or other LLM):

I'm going to attempt to use customer reviews to improve {Brand's} website content. Study the attached customer reviews. Filter out negative reviews from customers complaining or dissatisfied. Combine similar feedback into a single theme, using the most common phrase in the array as the primary theme; keep track of the quantity of items consolidated. Build semantic triple-style statements. Build prompts using natural language the way people normally speak. Output a list of the 100 most common themes into a spreadsheet, with columns for:

1. Review Theme (using the most common phrase in the array)
2. Number of Occurrences (sort table highest to lowest)
3. Semantic triple-style statement representing the theme as close as possible, starting with {Brand} as the subject.
4. Prompt written in natural language for a fast casual restaurant search that best aligns with the semantic triple-style statement.
5. Export to Excel

The output, with tracking-ready prompts for your AEO visibility monitoring:

Next, convert this data into business insights for the leadership to review and discuss.

Try this prompt using the original CSV export:

I have a CSV of customer reviews for {Brand}. Analyze the reviews and break them down into business improvement categories. Filter out negative reviews from dissatisfied customers, then analyze all reviews for sentiment signals across these categories:

- Product Quality
- Service Quality
- Operations
- Ambiance Quality
- Digital & Loyalty Experience
- Brand & Value Perception
- Staff & Culture

Add any additional categories you feel are missing that could guide business improvements.

For each category, identify specific themes within it. For every theme track:

- Positive mention count
- Negative mention count
- Total mentions
- Negative rate (%)
- Signal strength (High/Medium/Low based on volume)
- Improvement priority (Critical/High/Medium/Low based on negative rate)

Export to a multi-sheet Excel workbook with:

Sheet 1 - Executive Summary: Category-level totals with a key findings section calling out the most urgent issues
Sheet 2 - Theme Detail: Every theme across all categories in one sortable table
Sheet 3 - Category Breakdown: Each category in its own section with ranked themes
Sheet 4 - Priority Matrix: All themes sorted by improvement urgency, with auto-filter enabled

Use {Brand} brand colors (red #C8102E), assign a distinct color to each category, and color-code the priority column (Critical = red, High = orange, Medium = yellow, Low = green). Add data bars to volume columns and a color scale heatmap on the negative rate column.

The output you should see from the prompt above:

These few examples show how reviews can be powerful tools for SEO, AEO, and business development. That latter may not seem relevant to digital marketing, that is, until you see the click-through rate difference having 5 stars versus 3 stars in your Google Maps listing or rich snippet in Web Search.

Pillar 4 Action Items:

Establish a reputation-reporting framework; leverage AI for niche directories.
Harness reviews to address content gaps and uncover prompts to use for AEO tracking.
Use AI to turn reviews into opportunities for business improvement.

How AI Evaluates Local Businesses #

For as long as we remember, as far back as the days of the Google 7-Pack of map listings, local SEO strategies have centered around three things: relevancy, authority, and proximity signals.

LLMs, such as ChatGPT, introduced an entirely new layer to that strategy.

When your customer asks Google AI Mode, ChatGPT, Gemini, or Perplexity to recommend a business, the LLMs are doing more than simply retrieving a list of results. They are essentially evaluating any available proof it has that the data they uncovered is accurate, comparing alternative options, and producing recommendations based primarily on confidence and trust.

While each platform uses different technologies and data sources, they appear to share some common objectives: identify businesses that are both relevant to the prompt (often including personalization, previous interactions, and use of natural language) and supported by credible signals across the web (Pillars 3 and 4).

Entity-Based Search For MLSEO

The way we’ve viewed traditional multi-location SEO is that LLPs are the primary optimization focal point in our MLSEO strategy, where AI platforms appear to focus on the entity level.

A simple way to look at modern (AI-driven) SEO is using entity optimization as a north star. When we say entity, we mean any real-world thing, such as a business, a place, a person, specific products, or even an organization. Rather than crawling a single webpage, AI systems seem to try to understand how entities are related. In the early days of SEO, I tried to make my name semantic to the phrase “SEO expert.” I would query [steve wiideman], [seo expert] to see how many occurrences I had versus my competitors, using that number as my key performance indicator.

For multi-location SEO, a restaurant location may be connected to:

A parent brand.
A city or neighborhood.
Specific menu items.
Customer reviews.
Third-party directories.
Nearby landmarks.
Social media profiles.
Reservation platforms.

The more these connections (relationships) appear across the web, the easier it might be for search engines and AI systems to understand the business and the business’s role within a local market.

One useful way to think about AI visibility is through a trust triangle composed of three primary signal groups.

Business data: N.A.P., and other attributes, validated through multiple sources.Website content: Your brand-owned LLPs being the single source of truth.Third-party corroboration: Yelp, Tripadvisor, Avvo, Healthgrades, and local news coverage.

Some brands get cited, while others remain invisible – but why?

While supporting Meineke Car Care Centers, we occasionally had shop owners reach out about their rankings in Google. In nearly every case, we ran an audit and found reputation being the missing ranking attribute. Business data was on point, website content was best-in-class and even earning national recognition, but star ratings were often low, and online sentiment was not flattering. “Treating customers well” for an auto repair shop must have been a tough pill to swallow for those few locations that got away with it pre-internet.

Regardless of how great two of the three corners are, there’s no triangle unless all three corners are accounted for.

AI Evaluation Action Items:

Establish and lock down the entities the business should become semantic to.
Assign accountability to business data, LLP SEO, and third-party corroboration efforts.
Set specific goals for each and monitor progress quarterly (or monthly).

Measuring Visibility Beyond Rankings In Modern Search #

Traditional SEO reporting falls short in modern search due to the decay of the “keyword” and the need for users to land on our brand website to transact and interact with us when evolving platforms are working to keep users from needing to leave.

For the time being, we may continue to track traditional search, but should start experimenting with new metrics to better understand how our brand is performing overall.

Traditional Search Metrics (Still Important) | Modern Search Metrics | | |

These metrics just scratch the surface of foundational reporting for the modern search ecosystem. It’s critical to align visibility metrics with business objectives. For many franchise and multi-location restaurant chains, those business objectives are often off-premises (takeout, delivery, and catering) or menu-category pushes.

You’d never imagine Applebee’s as being known for “pasta and breadsticks,” yet they often outperform Olive Garden in organic search – that was a business objective.

Reporting Action Items:

Align with key stakeholders on specific business objectives and KPIs.
Decide what to track and where to track (Looker Studio, for example).
Set up reporting and a monthly/quarterly/annual reporting schedule.

Winning The Future Of Multi-Location Search #

A key theme you may have caught while reading this guide was the word “trust.” Establishing and sustaining trust with modern search platforms is the hallmark of a successful MLSEO strategy. We covered multiple ways of building trust through data consistency, data visibility, location pages, reputation, and sentiment. Each of these elements is trackable, measurable, and reportable.

This framework for multi-location visibility can be simplified in four steps:

Step 1: Audit Your Foundation

Work with your internal SEO team or a multi-location SEO consultant to audit your listings with your data management platform, your reviews and ratings, location, and intent/specialty pages, and online citations.

Establish your baseline and analyze the competitive landscape so you know what you’re up against.

Step 2: Strengthen Your Entity Signals

Work with your dev team to maximize the structured markup on your LLPs and intent pages. Establish an ongoing cadence with your data management platform and manual research (leveraging AI) to ensure online location data is consistent across the web.

Nurture a content strategy that tells visitors that your LLP is more helpful than anything the competition has to offer, including local and multimodal content. Develop intent pages that solve for specific business objectives, such as specials, off-premises, and specific product or service categories.

Step 3: Become The Authority In This Evolving Ecosystem

Work with brand and field teams to stimulate user-generated content. This may require creative experiments, such as wall art, table art, point of sale activities, or post-purchase follow-ups. Team up with the PR team to strategize ways to get local mentions, even if there is a layer of empowerment for the location owners/managers.

Review growth in brand authority regularly, holding all stakeholders accountable.

Step 4: Measure Across Search, Maps, And AI

Setting up reporting for AI may feel challenging at first. Here’s a tip: Drop this article into your favorite AI platform and try the following prompt:

Using the framework, KPIs, and recommendations in the document attached, design a comprehensive Data Studio dashboard for a multi-location brand. Recommend the most important metrics, dimensions, visualizations, filters, calculated fields, and data sources needed to measure visibility across Google Search, Google Maps, reputation platforms, industry directories, and AI-powered discovery experiences. Organize the dashboard for both executives and practitioners, explain why each metric matters, and provide a phased implementation roadmap based on impact and difficulty.

A Final Note #

Multi-location and franchise brands that win in AI-driven local search won’t necessarily be the brands with the highest rankings in Google. They’ll be the brands that establish themselves with the most trust, relevance, and strongest entity signals across Google, Maps, and AI-powered experiences.

This shift from rankings to recommendations may be the most important local search evolution in over a decade.

Earn trust by influencing the narrative about your brand and important entity relationships. Start by cleaning shop, and then by deploying programs that motivate customers to say things about their experiences that include the entities and adjectives that make our locations the ideal recommendations in modern search engines.

More Resources:

The Complete Guide To Local SEO For Multiple Locations The Death Of The Static GBP: Why Dynamic Profiles Are The New Local Ranking Factor Treating Reviews As Business Infrastructure, Not Marketing, Drives Real Business Results

Featured Image: Remo_Designer/Shutterstock

source & further reading

searchenginejournal.com — original article Google’s Liz Reid: Personalization Can Help Small Publishers Google Defends AI Training As Fair Use In Governance Paper Buying Reddit To Win AI Citations Is The New Link Farm